An Evaluation of Pharmacists‘ Expectations Towards Pharmacogenomics
Bibliographic record
Abstract
BACKGROUND: Given their expertise in pharmacotherapy, pharmacists are well positioned to play a leading role in the implementation of pharmacogenomics in clinical practice. However, little is known about the opinions of pharmacists towards pharmacogenomics or their willingness to integrate this new field in their practice. METHODS: We conducted a survey of 284 pharmacists practicing in the province of Québec (Canada) to describe the opinions, expectations and concerns of pharmacists toward pharmacogenomics. RESULTS: Pharmacists were very hopeful regarding the potential role of pharmacogenomics. Moreover, more than 95% of responders would be willing to recommend pharmacogenomic testing. Nevertheless, only 7.7% of pharmacists currently felt comfortable advising patients based on pharmacogenomic test results. Accordingly, the majority of responders (96.6%) indicated that they would like to undertake continuing education related to pharmacogenomics. CONCLUSION: Pharmacists are extremely hopeful towards pharmacogenomic testing. Furthermore, a vast majority is willing to integrate these tests as part of their clinical practice. Proper education will be required if the integration of pharmacogenomics in patient care is to be optimal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".